Submission¶

Put the ipynb file and html file in the github branch you created in the last assignment and submit the link to the commit in brightspace

In [1]:
from plotly.offline import init_notebook_mode
import plotly.io as pio
import plotly.express as px

init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"
In [2]:
#load data
df = px.data.gapminder()
df.head()
Out[2]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4

Question 1:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [3]:
sum_df = df.query("year==2007").groupby('continent').sum()
fig = px.bar(sum_df, x='pop', y=sum_df.index, orientation='h', color=sum_df.index) # set the params
fig.show() # show the figure
In [5]:
# YOUR CODE HERE
# to preserve the original plot the code is above

Question 2:¶

Sort the order of the continent for the visualisation

Hint: Use axis layout setting

In [9]:
fig.update_yaxes(categoryorder='total ascending') # axis layout setting
fig.show() # show the figure
In [6]:
# YOUR CODE HERE
# to preserve the original plot the code is above

Question 3:¶

Add text to each bar that represents the population

In [13]:
fig = px.bar(sum_df, x='pop', y=sum_df.index, orientation='h', color=sum_df.index, text='pop', text_auto='.2s') # add text
fig.update_yaxes(categoryorder='total ascending')
fig.update_traces(textposition="outside") # set the position of texts
fig.show()
In [7]:
# YOUR CODE HERE
# to preserve the original plot the code is above

Question 4:¶

Thus far we looked at data from one year (2007). Lets create an animation to see the population growth of the continents through the years

In [15]:
sum_df = df.groupby(['continent','year']).sum()
sum_df.reset_index(inplace = True)
fig = px.bar(sum_df, x='pop', y='continent', orientation='h', color='continent', animation_frame='year', range_x = [0,4000000000])
fig.update_yaxes(categoryorder='total ascending')
fig.show()
In [9]:
# YOUR CODE HERE
# to preserve the original plot the code is above

Question 5:¶

Instead of the continents, lets look at individual countries. Create an animation that shows the population growth of the countries through the years

In [16]:
fig = px.bar(df, x='pop', y='country', orientation='h', color='country', animation_frame='year', range_x = [0,1500000000])
fig.update_yaxes(categoryorder='total ascending')
fig.show()
In [11]:
# YOUR CODE HERE
# to preserve the original plot the code is above

Question 6:¶

Clean up the country animation. Set the height size of the figure to 1000 to have a better view of the animation

In [17]:
fig = px.bar(df, x='pop', y='country', orientation='h', color='country', animation_frame='year', range_x = [0,1500000000], height=1000)
fig.update_yaxes(categoryorder='total ascending')
fig.show()
In [12]:
# YOUR CODE HERE
# to preserve the original plot the code is above

Question 7:¶

Show only the top 10 countries in the animation

Hint: Use the axis limit to set this.

In [19]:
fig = px.bar(df, x='pop', y='country', orientation='h', color='country', \
animation_frame='year', range_x = [0,1500000000], range_y=[132.5,141.5])
fig.update_yaxes(categoryorder='total ascending')
fig.show()
In [13]:
# YOUR CODE HERE
# to preserve the original plot the code is above